NLP can be used to great effect in a variety of business operations and processes to make them more efficient. One of the best ways to understand NLP is by looking at examples of natural language processing in practice. Let’s look at the common NLP examples in more detail.
1. Online Search Engines
When it comes to examples of natural language processing, search engines are probably the most common. When a user uses a search engine to perform a specific search, the search engine uses an algorithm to not only search web content based on the keywords provided but also the intent of the searcher. In other words, the search engine “understands” what the user is looking for. For example, if a user searches for “apple pricing” the search will return results based on the current prices of Apple computers and not those of the fruit.
2. Email Filters
Email filtering is also one of the common examples of natural language processing. An NLP algorithm is trained on email data and is then able to identify regular, spam, promotion, social media-related emails. Depending on the specific email provider, email filters are also able to identify internal or external emails in an office environment. This makes email filtering a valuable tool to ensure that email users only get the emails they should and, more importantly, serves as an important line of defense against malicious emails which, in turn, protects an organization’s systems against data breaches.
3. Virtual Assistants, Voice Assistants, or Smart Speakers
Another one of the common NLP examples is voice assistants like Siri and Cortana that are becoming increasingly popular. These assistants use natural language processing to process and analyze language and then use natural language understanding (NLU) to understand the spoken language. Finally, they use natural language generation (NLG) which gives them the ability to reply and give the user the required response. Voice command activated assistants still have a long way to go before they become secure and more efficient due to their many vulnerabilities, which data scientists are working on.
4. Semantic Knowledge Management
Semantic knowledge management systems allow organizations to store, classify, and retrieve knowledge that, in turn, helps them improve their processes, collaborate within their teams, and improve understanding of their operations. Here, one of the best NLP examples is where organizations use them to serve content in a knowledge base for customers or users. See how Repustate helped GTD semantically categorize, store, and process their data.
5. Predictive Text and Autocorrect
One of the most prevalent examples of natural language processing is predictive text and autocorrect. NLP ensures that every time a mobile phone user types text on their smartphone, it will suggest what they intended to type. Predictive text and autocorrect are also helpful tools in word processors like Microsoft Word, where they can allow users to work faster and more accurately. People use predictive text from writing formal and effective work emails, to completing an entire thesis - because it just makes things faster than the time taken otherwise. Autocorrects is another ball game. Where they are very helpful in one way, they are notorious for misspelling things because they don’t take into account context, resulting in hilarious fails.
6. Brand Sentiment Monitoring on Social Media
With social media listening, businesses can understand what their customers and others are saying about their brand or products on social media. NLP helps social media sentiment analysis to recognize and understand all types of data including text, videos, images, emojis, hashtags, etc. Through this enriched social media content processing, businesses are able to know how their customers truly feel and what their opinions are. In turn, this allows them to make improvements to their offering to serve their customers better and generate more revenue. Thus making social media listening one of the most important examples of natural language processing for businesses and retailers.
7. Sorting Customer Feedback
NLP can help businesses in customer experience analysis based on certain predefined topics or categories. It’s able to do this through its ability to classify text and add tags or categories to the text based on its content. In this way, organizations can see what aspects of their brand or products are most important to their customers and understand sentiment about their products.
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NLP (Natural Language Processing) is a subset of Artificial Intelligence.
Both large and small businesses can implement next-generation Customer Experience tools which will leverage AI-based NLP conversational interfaces to improve the customer experience at a reduced cost.Legal Industry: Natural Language Processing is playing an increasing role in finding information relevant to judicial decisions, determining related documents, generating routine legal reports, and provides customized advice using the Q feature.Healthcare Sector: Natural Language Processing algorithms can read all sorts of data in the healthcare space to find the best ways to extract relevant inputs and bring it together to help healthcare workers make better decisions for their patients.Benefits of Natural Language Processing:Natural Language Processing has gone beyond being merely a better input method.
Given the marketing is heavily susceptible to words to convey messages to the users about products and services, it is no surprise that Natural Language Processing has played a nice role in niche marketing technology.Let’s see where all NLP is playing a nice role.
Chatbots for Capturing LeadsA chatbot or a pop-up, it’s almost on all the websites, right?
Currently, the chatbots are able to answer the readymade questions by figuring out through the keywords but when it turns into a complex conversation, they rely on human customer support.Voice Search for Gaining Access to a Wider AudienceIn recent years, voice search has become incredibly powerful and popular.
Here are some of the highlights listed below that every sphere going to like:#1.
Saves human staff/resourcesBots don’t get tired of answering repetitive questions, or inquiries like human staff do.
Quicker processesAn ai chatbot isn’t limited by the number of conversations and can respond to multiple prospects simultaneously.
It makes the whole process more efficient and faster.How do they Function?Now let’s discuss how exactly chatbots function and what helps in smart Chatbot development.
The moment any question or query is presented to a bot, complex algorithms process the input and understand the query’s right tone and context.
Whereas, during the Chatbot development process, your bot’s intelligence will depend on the way you apply machine learning while deploying the bot in your systems.Moreover, if you want to know deeply about the whole Chatbot development process, it is not enough to know only about technology.
As human-generated data has exponentially increased on many platforms, it has become impossible for the businesses to check or interact with that data manually, here the applications of NLP take charge.For example, if a business wants to check the reviews of their new product and make inferences from it, allotting people for reading the reviews and categorizing them will be inefficient, as there are lakhs of reviews.
These chatbots can be customized for a specific market like Kotak Mahindra Bank now uses Keya as its virtual assistant.How NLP works?Making sense of the syntax is one of the most critical things for applying NLP algorithms; some of the syntax analysis methods are:Lemmatization: It means reducing the various inflected forms of a word into a single form for a more straightforward analysis.Morphological segmentation: It involves dividing words into individual units called morphemes.Word segmentation: It consists of dividing a large piece of continuous text into distinct units.Part-of-speech tagging: It involves identifying the part of speech for every word.Parsing: It consists in undertaking a grammatical analysis for the provided sentence.Sentence breaking: It consists in placing sentence boundaries on a large piece of text.Stemming: It involves cutting the inflected words to their root form.Morphological segmentation: It consists of dividing words into individual units called morphemes.Word segmentation: It consists of dividing a large piece of continuous text into distinct units.Part-of-speech tagging: It involves identifying the part of speech for every word.Parsing: It involves undertaking a grammatical analysis for the provided sentence.Sentence breaking: It involves placing sentence boundaries on a large piece of text.Stemming: It involves cutting the inflected words to their root form.Open-source libraries for NLPThere are many NLP libraries on the internet, some of the most popular libraries are:NLTK: It is the most featured library; the most used combination is nltk(Natural Language Toolkit) and python.
This library is for production usage, written in the programming language Cython; it provides the fastest syntactic parser in the market.
Although the menu of this library is limited, there are fewer choices.TextBlob: This library is an extension of nltk.
This library is suitable for smaller projects.Saas Tools for NLPPeople want to generate insights from texts, but don’t want to delve deeper into the working, some of the tools for non-technical people are:Amazon Comprehend: Amazon Comprehend is a natural language processing software that uses machine learning to look for insights and relationships within a text.
Advertising companies analyze the digital footprints of a user to predict their potential audience interested in their products.NLP software helps increase the range of channels for ad placement.Healthcare is entering a new era with NLP applications, data mining integration in healthcare systems helps doctors make more well-informed decisions and improve diagnosis, and patient treatment.Chatbots are a prime example of automation tech.
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